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Shock and Vibration
Volume 20 (2013), Issue 4, Pages 763-780

Multi-Fault Feature Extraction and Diagnosis of Gear Transmission System Using Time-Frequency Analysis and Wavelet Threshold De-Noising Based on EMD

Renping Shao, Wentao Hu, and Jing Li

School of Mechatronics, Northwestern Polytechnical University, Xi’an, Shaanxi, China

Received 26 January 2012; Revised 4 August 2012; Accepted 19 October 2012

Copyright © 2013 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


gear transmission system is a complex non-stationary and nonlinear time-varying coupling system. When faults occur on gear system, it is difficult to extract the fault feature. This paper researches the threshold principle in the process of using the wavelet transform to de-noise the system, and combines EMD (empirical mode decomposition) with wavelet threshold de-noising to solve the problem. The wavelet threshold de-noising is acts on the high-frequency IMF (Intrinsic Mode Function) component of the signal, and does overcome the defect by simply highlighting the fault feature. On this basis, the pre-processed signal is analyzed by the method of time-frequency analysis to extract the feature of the signal. The result shows that the SNR (signal-noise ratio) of the signal was largely improved, and the fault feature of the signal can therefore be effectively extracted. Combined with time-frequency analyses in the different running conditions (300 rpm, 900 rpm), various faults such as tooth root crack, tooth wear and multi-fault can be identified effectively. Based on this theory and combining the merits of MATLAB and VC++, a multi-functional gear fault diagnosis software system is successfully exploited.